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A fuzzy based chicken swarm optimization algorithm for efficient fault node detection in Wireless Sensor Networks.

B Nagarajan¹, Santhosh Kumar Svn², M Selvi³

¹School of computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, India. nagarajan.b@vit.ac.in.

Scientific Reports

|November 11, 2024

Summary

This summary is machine-generated.

This study introduces a novel algorithm for detecting sensor node failures in Wireless Sensor Networks (WSN). The proposed method enhances fault detection accuracy and network performance while minimizing energy consumption and false alarms.

Keywords:

Fault detection Node fault Optimization Poisson Hidden Markov model Sensor node

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Area of Science:

Computer Science
Electrical Engineering
Network Engineering

Background:

Wireless Sensor Networks (WSN) are crucial for environmental monitoring but suffer from sensor node failures due to deployment challenges and limited resources.
Node failures in WSN can lead to topology changes, communication disruptions, network partitioning, and data transmission errors.
Effective detection, diagnosis, and recovery of sensor node failures are critical for WSN reliability.

Purpose of the Study:

To propose an effective sensor node failure detection algorithm for Wireless Sensor Networks (WSN).
To improve the accuracy of fault detection, reduce false alarms and false positives, and optimize energy consumption.
To enhance the localization of defective sensor nodes within the WSN.

Main Methods:

A novel algorithm combining the Poisson Hidden Markov Model (PHMM) for fault detection and Fuzzy-based Chicken Swarm Optimization (F-CSO) for improved localization and parameter optimization.
Implementation and simulation using the NS2 simulator with realistic parameters to evaluate performance.
Comparison with existing state-of-the-art systems to demonstrate effectiveness.

Main Results:

Achieved 89.5% fault detection accuracy, a significant improvement over existing methods.
Demonstrated a 19.53% increase in throughput and an 8.43% reduction in energy consumption.
Showcased minimal delay rates and a lower false positive rate compared to other systems.

Conclusions:

The proposed PHMM and F-CSO based algorithm offers a highly effective solution for detecting sensor node failures in WSN.
The approach significantly enhances network performance metrics including accuracy, throughput, and energy efficiency.
This method provides a robust strategy for maintaining WSN integrity and reliability in the face of node failures.